Early initiation and regular breast milk expression reduces risk of lactogenesis II delay in at-risk Singaporean mothers in a randomised trial

INTRODUCTION@#Lactogenesis II (LaII) failure can be prevented in at-risk mothers with simple proactive interventions. In a randomised trial, we investigated the efficacy of early and regular breast milk expression in establishing LaII, using an electric double-breast pump.@*METHODS@#Mothers with uncomplicated singleton deliveries were randomised to intervention (n = 31) or control (n = 29) groups. The former commenced breast milk expression with an electric pump within one hour of delivery and maintained regular expression with direct breastfeeding. Control mothers directly breastfed without regular pump expression. Expressed milk volumes were analysed for citrate, lactose, sodium and protein.@*RESULTS@#Median time of LaII was Day 3 (interquartile range [IQR] 1 day) with intervention and on Day 4 (IQR 1 day) among controls (p = 0.03). Biochemical steady-state concentrations were achieved around early Day 4 (sodium, total protein) and Days 4-5 (citrate, lactose). Sodium, protein and lactose levels were similar in both groups over seven days, at 5.80 mM, 0.68 mM and -13.38 mM, respectively. Mean daily milk volume with intervention was 73.9 mL on Day 3 and 225.2 mL on Day 7, greater than controls (25.4 mL on Day 3 and 69.2 mL on Day 7; p < 0.2). Mean infant weights were similar on Day 8 at 3,477 g with intervention and 3,479 g among controls.@*CONCLUSION@#LaII is established by postnatal Day 3 with early initiation of regular breast milk expression, a useful intervention for mothers at risk of early-onset breastfeeding failure.

HEDEA: A Python Tool for Extracting and Analysing Semi-structured Information from Medical Records / 대한의료정보학회지

OBJECTIVES: One of the most important functions for a medical practitioner while treating a patient is to study the patient's complete medical history by going through all records, from test results to doctor's notes. With the increasing use of technology in medicine, these records are mostly digital, alleviating the problem of looking through a stack of papers, which are easily misplaced, but some of these are in an unstructured form. Large parts of clinical reports are in written text form and are tedious to use directly without appropriate pre-processing. In medical research, such health records may be a good, convenient source of medical data; however, lack of structure means that the data is unfit for statistical evaluation. In this paper, we introduce a system to extract, store, retrieve, and analyse information from health records, with a focus on the Indian healthcare scene. METHODS: A Python-based tool, Healthcare Data Extraction and Analysis (HEDEA), has been designed to extract structured information from various medical records using a regular expression-based approach. RESULTS: The HEDEA system is working, covering a large set of formats, to extract and analyse health information. CONCLUSIONS: This tool can be used to generate analysis report and charts using the central database. This information is only provided after prior approval has been received from the patient for medical research purposes.

Classification of Peroxiredoxin Subfamilies Using Regular Expressions

Peroxiredoxins (Prx's) are a superfamily of peroxidases that are ubiquitous in all super-kingdoms. Previous biochemical and structural studies have suggested that Prx's could be divided into five subfamilies (1-Cys, Typical 2-Cys, Atypical 2-Cys C-, L- and R- types). In this work, we have developed a set of regular expression patterns describing subfamily-specific spatial constraints of the key catalytic residues. Using these patterns, 1,016 Prx's available in public databases were classified into the five subfamilies. Our method performed well for most of the types except for Atypical 2 Cys R type.